





'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase					    =	logging.get_logger(__name__)

__UpperCamelCase					    =	{
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}




class  _A (					__lowercase		):
		lowercase__:   str				     =   '''ibert'''

		def __init__(   self					:   str  ,      __magic_name__					:   str=3_05_22  ,      __magic_name__					:   List[str]=7_68  ,      __magic_name__					:   List[Any]=12  ,      __magic_name__					:   Union[str, Any]=12  ,      __magic_name__					:   int=30_72  ,      __magic_name__					:   Dict="gelu"  ,      __magic_name__					:   Union[str, Any]=0.1  ,      __magic_name__					:   Dict=0.1  ,      __magic_name__					:   int=5_12  ,      __magic_name__					:   str=2  ,      __magic_name__					:   List[Any]=0.02  ,      __magic_name__					:   Optional[Any]=1E-12  ,      __magic_name__					:   Optional[Any]=1  ,      __magic_name__					:   str=0  ,      __magic_name__					:   Union[str, Any]=2  ,      __magic_name__					:   Optional[int]="absolute"  ,      __magic_name__					:   Dict=False  ,      __magic_name__					:   Any="none"  ,      **__magic_name__					:   Union[str, Any]  ,      )					->    Union[str, Any]:


					"""simple docstring"""

					super().__init__(pad_token_id=__magic_name__  ,      bos_token_id=__magic_name__  ,      eos_token_id=__magic_name__  ,      **__magic_name__			)

					__snake_case					:      Tuple							      =     vocab_size
					__snake_case					:      Tuple							      =     hidden_size
					__snake_case					:      List[Any]							      =     num_hidden_layers
					__snake_case					:      str							      =     num_attention_heads
					__snake_case					:      Optional[int]							      =     hidden_act
					__snake_case					:      int							      =     intermediate_size
					__snake_case					:      Any							      =     hidden_dropout_prob
					__snake_case					:      List[str]							      =     attention_probs_dropout_prob
					__snake_case					:      str							      =     max_position_embeddings
					__snake_case					:      List[Any]							      =     type_vocab_size
					__snake_case					:      int							      =     initializer_range
					__snake_case					:      Union[str, Any]							      =     layer_norm_eps
					__snake_case					:      List[Any]							      =     position_embedding_type
					__snake_case					:      Optional[int]							      =     quant_mode
					__snake_case					:      Optional[int]							      =     force_dequant





class  _A (					__lowercase		):

		@property
		def 	lowercase__     (   self					:   Optional[Any]			)					->    Mapping[str, Mapping[int, str]]:


					"""simple docstring"""

					if self.task == "multiple-choice":
								__snake_case					:      List[Any]							      =     {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
								__snake_case					:      str							      =     {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					    ]			)










"""simple docstring"""





from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest






@require_tokenizers
class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					DistilBertTokenizer
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					DistilBertTokenizerFast
    SCREAMING_SNAKE_CASE		:	int		=					True






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						int:
      lowerCamelCase_       	=		DistilBertTokenizer.from_pretrained('distilbert-base-uncased'   )

      lowerCamelCase_       	=		tokenizer.encode('sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode('multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
          tokenizer.sep_token_id
      ]




